As noted earlier, exploration is one of the features that differentiates RL from other standard learning problems. RL imposes the need to seek information actively, testing out novel behaviors and balancing them against established knowledge, negotiating the explore-exploit trade-off. Animals, of course, face this challenge as well, and it has been of considerable interest in neuroscience and psychology [1]}, [2]}, [3]}, [4]}. Here once again, deep RL offers a new computational perspective and a set of specific algorithmic ideas.
